Trends in work-related respiratory diseases attributed to nickel, chromium and cobalt in the UK: descriptive findings from The Health and Occupation Research (THOR) network 1996-2019.

BACKGROUND: Occupational exposure to metals can be associated with respiratory diseases which can adversely affect the individual's health, finances and employment. Despite this, little is known about the incidence of these respiratory conditions over prolonged periods of time. AIMS: This study aimed to investigate the trends in the incidence of work-related respiratory diseases attributed to nickel, chromium and cobalt in the UK. METHODS: Cases of occupational respiratory diseases caused by nickel, chromium or cobalt reported to Surveillance of Work-related and Occupational Respiratory Disease (SWORD), the UK-based surveillance scheme between 1996 and 2019 (inclusive), were extracted and grouped into six 4-year time periods. Cases were characterised by causative metal exposure, occupational and industrial sector. Incidence rates diseases (adjusted for physician participation and response rate) were calculated using ONS employment data. RESULTS: Of cases reported to SWORD during the study period, 1% (173 actual cases) of respiratory problems were attributed to nickel, chromium or cobalt. Diagnoses of asthma compromised the largest proportion of diagnoses (74.4%), followed by lung cancer (8.9%) and pneumoconiosis (6.7%). Cases had a mean age of 47 years (SD 13); 93% were men. The annual incidence fell from 1.6 per million employed in the first 4-year period, to 0.2 in the most recent period. CONCLUSIONS: Over 24 years, a decline in the incidence of metal-related occupational respiratory diseases was observed in the UK. This could be attributed to improvements in working conditions which resulted in reduced metal exposure but could also be due to closure of industries that might have generated case returns.

Risk factors for SARS-CoV-2 infection at a UK electricity-generating company: a test-negative design case-control study.

OBJECTIVES: Identify workplace risk factors for SARS-CoV-2 infection, using data collected by a UK electricity-generating company. METHODS: Using a test-negative design case-control study, we estimated the OR of infection by job category, site, test reason, sex, vaccination status, vulnerability, site outage and site COVID-19 weekly risk rating, adjusting for age, test date and test type. RESULTS: From an original 80 077 COVID-19 tests, there were 70 646 included in the final analysis. Most exclusions were due to being visitor tests (5030) or tests after an individual first tested positive (2968).Women were less likely to test positive than men (OR=0.71; 95% CI 0.58 to 0.86). Test reason was strongly associated with positivity and although not a cause of infection itself, due to differing test regimes by area, it was a strong confounder for other variables. Compared with routine tests, tests due to symptoms were highest risk (94.99; 78.29 to 115.24), followed by close contact (16.73; 13.80 to 20.29) and broader-defined work contact 2.66 (1.99 to 3.56). After adjustment, we found little difference in risk by job category, but some differences by site with three sites showing substantially lower risks, and one site showing higher risks in the final model. CONCLUSIONS: In general, infection risk was not associated with job category. Vulnerable individuals were at slightly lower risk, tests during outages were higher risk, vaccination showed no evidence of an effect on testing positive, and site COVID-19 risk rating did not show an ordered trend in positivity rates.

Potential contribution of vaccination uptake to occupational differences in risk of SARS-CoV-2: analysis of the ONS COVID-19 Infection Survey.

OBJECTIVES: To assess variation in vaccination uptake across occupational groups as a potential explanation for variation in risk of SARS-CoV-2 infection. DESIGN: We analysed data from the UK Office of National Statistics COVID-19 Infection Survey linked to vaccination data from the National Immunisation Management System in England from 1 December 2020 to 11 May 2022. We analysed vaccination uptake and SARS-CoV-2 infection risk by occupational group and assessed whether adjustment for vaccination reduced the variation in risk between occupational groups. RESULTS: Estimated rates of triple vaccination were high across all occupational groups (80% or above), but were lowest for food processing (80%), personal care (82%), hospitality (83%), manual occupations (84%) and retail (85%). High rates were observed for individuals working in health (95% for office based, 92% for those in patient-facing roles) and education (91%) and office-based workers not included in other categories (90%). The impact of adjusting for vaccination when estimating relative risks of infection was generally modest (ratio of hazard ratios across all occupational groups reduced from 1.37 to 1.32), but was consistent with the hypothesis that low vaccination rates contribute to elevated risk in some groups. CONCLUSIONS: Variation in vaccination coverage might account for a modest proportion of occupational differences in infection risk. Vaccination rates were uniformly very high in this cohort, which may suggest that the participants are not representative of the general population. Accordingly, these results should be considered tentative pending the accumulation of additional evidence.

Inconsistent health hazard information across safety data sheets for substances in cleaning products used in healthcare centres.

Safety Data Sheets (SDSs) are used to inform downstream users of any hazardous substances in chemical products and advise on how to manage the risks from using these products. It is therefore important that information on the SDS is accurate and consistent. This study investigates the accuracy and consistency of hazard information included in the SDSs of cleaning products used in the healthcare sector in England and Wales. Data on cleaning products used in the National Health Service (NHS) in England and Wales and their chemical composition and any hazard information (as H-statements) were collected from the products' SDSs obtained from the NHS supply online catalogue. By each hazard, mainly respiratory hazards, the number of hazardous substances specified as hazardous in all SDSs was identified. Moreover, we investigated hazard characteristics of substances identified by only SDS (at least one SDS) or only through Harmonised Classifications and Labeling (CLH) or by SDS and through CLH simultaneously. In total, 229 unique chemical substances were found in 473 cleaning products' SDSs. All 4 respiratory sensitisiers were identified in all SDSs and through CLH. However, only 14 of the 25 respiratory irritants (56.0 %) were consistently labelled across all SDSs. Although respiratory irritation characteristics of 3 substances were classified through CLH, it was not identified by any of the relevant SDSs. Substantially incorrect and inconsistent health hazard information for the same substances was identified across SDSs. Therefore, healthcare workers and their managers may not receive accurate information on the presence of and potential for exposure to hazardous substances in the cleaning products they are using.

Estimating occupational disease burden: a way forward.

Estimates of occupational disease burden provide important information on which effective policy and regulations can be developed. However, there is no direct way that these data can be obtained, and most burden estimates are derived by merging different data from diverse sources to synthesize estimates of the number of people made ill or who have died from workplace exposures. In recent years, several research groups have published estimates of occupational health burden at national or global scales; these are not always consistent. The World Health Organisation and the International Labour Organisation have taken on the task of producing occupational disease burden estimates for several workplace agents, which we assume are to be seen as the definitive global, regional, and national data. In this commentary, we critique the WHO/ILO approach for their estimates of the non-melanoma skin cancer burden from solar ultraviolet radiation and some of their results for hazardous particulates. We provide recommendations for researchers undertaking occupational burden estimates that they should report along with their data.

The application of the mobile application for the assessment of cleaning workers' exposure to cleaning products: a pilot study.

BACKGROUND: Cleaning product use has been associated with adverse respiratory health effects such as asthma in cleaning staff and healthcare workers. Research in health effects from cleaning products has largely depended upon collecting exposure information by questionnaires which has limitations such as recall bias and underestimation of exposure. The aim of this study was to develop a Cleaning and Hazardous Products Exposure Logging (CHaPEL) app with a barcode scanner and to test the feasibility of this app with university cleaners. METHODS: The CHaPEL app was developed to collect information on demographics, individual product information, and exposure information. It also included an ease-of-use survey. A pilot study with university cleaning workers was undertaken in which cleaning workers scanned each product after use and answered the survey. Respiratory hazards of cleaning substances in the scanned cleaning products were screened by safety data sheets, a Quantitative Structure-Activity Relationship model and an asthmagen list established by an expert group in the US. RESULTS: Eighteen university cleaners participated in this study over a period of 5 weeks. In total, 77 survey responses and 6 cleaning products were collected and all reported that using the app was easy. The most frequently used product was a multi-surface cleaner followed by a disinfectant. Out of 14 substances in cleaning products, ethanolamine and Alkyl (C12-16) dimethyl benzyl ammonium chloride were found as respiratory hazardous substances. CONCLUSION: The CHaPEL app is a user-friendly immediate way to successfully collect exposure information using the barcodes of cleaning products. This tool could be useful for future epidemiological studies focused on exposure assessment with less interruption to the workers.

Occupational exposure to pesticides and neurobehavioral outcomes. Impact of different original and recalled exposure measures on the associations.

BACKGROUND: Several measures of occupational exposure to pesticides have been used to study associations between exposure to pesticides and neurobehavioral outcomes. This study assessed the impact of different exposure measures for glyphosate and mancozeb on the association with neurobehavioral outcomes based on original and recalled self-reported data with 246 smallholder farmers in Uganda. METHODS: The association between the 6 exposure measures and 6 selected neurobehavioral test scores was investigated using linear multivariable regression models. Exposure measures included original exposure measures for the previous year in 2017: (i) application status (yes/no), (ii) number of application days, (iii) average exposure-intensity scores (EIS) of an application and (iv) number of EIS-weighted application days. Two additional measures were collected in 2019: (v) recalled application status and (vi) recalled EIS for the respective periods in 2017. RESULTS: Recalled applicator status and EIS were between 1.2 and 1.4 times more frequent and higher for both pesticides than the original application status and EIS. Adverse associations between the different original measures of exposure to glyphosate and 4 neurobehavioral tests were observed. Glyphosate exposure based on recalled information and all mancozeb exposure measures were not associated with the neurobehavioral outcomes. CONCLUSIONS: The relation between the different original self-reported glyphosate exposure measures and neurobehavioral test scores appeared to be robust. When based on recalled exposure measures, associations observed with the original exposure measures were no longer present. Therefore, future epidemiological studies on self-reported exposure should critically evaluate the potential bias towards the null in observed exposure-response associations.

A Wrist-Worn Internet of Things Sensor Node for Wearable Equivalent Daylight Illuminance Monitoring.

Light exposure is a vital regulator of physiology and behavior in humans. However, monitoring of light exposure is not included in current wearable Internet of Things (IoT) devices, and only recently have international standards defined [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of EDI measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured [Formula: see text]-opic EDI results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT-based lighting control.

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.

An individual's likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.